code: def test_openvino_gpu_quantize(self):
      # test whether contains GPU
      from openvino.runtime import Core
  
      core = Core()
      devices = core.available_devices
      gpu_avaliable = any("GPU" in x for x in devices)
      model = mobilenet_v3_small(num_classes=10)
  
      x = torch.rand((1, 3, 256, 256))
      x2 = torch.rand((10, 3, 256, 256))
  
      # test dynamic_shape
      with pytest.raises(
          RuntimeError,
          match="For model has dynamic axes, if you want to inference on non-CPU device, must define "
          "input_shape for model optimizer. For more details about model optimizer, you can see mo --help .",
      ):
          openvino_model = InferenceOptimizer.quantize(
              model,
              input_sample=x,
              accelerator="openvino",
              precision="fp16",
              device="GPU",
          )
      with pytest.raises(
          RuntimeError,
          match="For model has dynamic axes, if you want to inference on non-CPU device, must define "
          "input_shape for model optimizer. For more details about model optimizer, you can see mo --help .",
      ):
          openvino_model = InferenceOptimizer.quantize(
              model,
              input_sample=x,
              accelerator="openvino",
              dynamic_axes={"x": [0]},
              precision="fp16",
              device="GPU",
          )
  
      if gpu_avaliable is False:
          with pytest.raises(RuntimeError):
              openvino_model = InferenceOptimizer.quantize(
                  model,
                  input_sample=x,
                  accelerator="openvino",
                  device="GPU",
                  input_shape="[1,3,256,256]",
                  precision="fp16",
              )
          return
  
      # test GPU fp16
      openvino_model = InferenceOptimizer.quantize(
          model,
          input_sample=x,
          accelerator="openvino",
          device="GPU",
          precision="fp16",
      )
      result = openvino_model(x)
      # GPU don't support dynamic shape
      with pytest.raises(RuntimeError):
          openvino_model(x2)
  
      # test GPU int8
      openvino_model = InferenceOptimizer.quantize(
          model,
          calib_data=x,
          accelerator="openvino",
          device="GPU",
          precision="int8",
      )
      result = openvino_model(x)
      # GPU don't support dynamic shape
      with pytest.raises(RuntimeError):
          openvino_model(x2)
  
imports: from unittest import TestCase
  from torchmetrics import F1Score
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch import InferenceOptimizer
  from torchvision.models.mobilenetv3 import mobilenet_v3_small
  from torchvision.models import resnet50
  import torch
  from torch import nn
  from torch.utils.data.dataset import TensorDataset
  from torch.utils.data.dataloader import DataLoader
  import tempfile
  import pytest
  import numpy
label_desc: Use image node_id 9 for calling the TestOpenVINO obj's underlying member method code with example usage: obj.test_openvino_gpu_quantize() without return types